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Ann Arbor ASA ‘Up and Running’ Series: The SAS System

Ann Arbor ASA ‘Up and Running’ Series: The SAS System. Sponsored by The Department of Statistics and the Ann Arbor Chapter of American Statistical Association,. Contents. Starting SAS User Interface Libraries Syntax Getting Data into SAS Examining Data Manipulating Data

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Ann Arbor ASA ‘Up and Running’ Series: The SAS System

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  1. Ann Arbor ASA‘Up and Running’ Series:The SAS System Sponsored by The Department of Statistics and the Ann Arbor Chapter of American Statistical Association,

  2. Contents • Starting SAS • User Interface • Libraries • Syntax • Getting Data into SAS • Examining Data • Manipulating Data • Descriptive Statistics • Graphing Data • Statistics in SAS

  3. Starting SAS Start=>All Programs=> Math & Statistics=>SAS =>SAS 9.2 (32) English

  4. User Interface Log Comments, warnings, etc. Program Editor: Write and submit commands Explorer/Results Output (not seen)

  5. Libraries • SAS requires the creation of Library folders to save the data • Libraries are accessed through LIBREF or LIBNAME commands • Four Libraries are defined by default, at the start of SAS • Maps • SASHELP: holds help info and sample datasets • SASUSER: holds settings, etc. • WORK: default temporary Library for each session • All data stored in this folder will be deleted at the end of each SAS session • It is recommended the creation of permanent files/Libraries

  6. Libraries • Create a folder called ‘My_Files’ on your desktop. • Run this command in SAS: LIBNAME a "C:\Users\uniquename\Desktop\My_Files"; • Refer to datasets in that folder by with the prefix ‘a.datasetname’. • TIP: Use memorable names for libraries, rather than ‘a’ (e.g., ‘raw’, ‘final’, ‘time1’, etc)

  7. Syntax • SAS divides commands into two groups • DATA step • create/alter datasets • PROC (Procedures) • perform statistical analyses or generate reports. • Some exceptions to the rule: • DATA step can be used to generate reports • PROC IMPORT creates a data set • PROC SORT alters data sets (without telling you!)

  8. Getting data into SAS • PROC IMPORT • Allows the reading of standard file types • Allows the reading of plain text, with user-specified delimiters (i.e., the characters which separate the data) • DATA step • Allows the reading of non-standard file types, complex file structures, and unusual delimiters.

  9. PROC IMPORT • Place pointer in Editor window • In Menu Bar: File  Import data • Follow the wizard • When asked if you want SAS to save the syntax to a file, click “Browse...” and give a filename • SAS will generate and run the syntax • Examine Log for errors

  10. PROC IMPORT

  11. PROC IMPORT • When asked if you want SAS to save the syntax to a file, • click “Browse...” and give a filename

  12. PROC IMPORT

  13. PROC IMPORT • Examine Log for errors

  14. PROC IMPORT

  15. PROC IMPORT • For example, SAS generated the following syntax: PROC IMPORT OUT= WORK.class2 DATAFILE= "C:\Desktop\class2.xls" DBMS=EXCEL5 REPLACE; GETNAMES=YES; RUN; • Examine and understand the syntax - you’ll learn more!

  16. DATA step • SAS syntax can be used to read in raw data files (.txt, .csv files), specifying which variables to read in, which ones are text/numeric, combining multiple rows into one case, etc. • However, this is a more advanced topic. • Follow up with an Intro class from CSCAR, or by going through examples from the literature (e.g., ‘The Little SAS Book’).

  17. Examining Data • VIEWTABLE Window • Select dataset icon in Explorer • PROC CONTENTS • Produces a listing of data set information, including the variables and their properties • PROC PRINT • Prints a subset of variables or cases to the output window

  18. VIEWTABLE Window

  19. PROC CONTENTS • In the Editor window, type: proc contents data=a.class2; run; • Highlight the syntax • Submit for processing • Click on icon of ‘running-man’ • Right click on selected syntax  Submit Selection

  20. PROC CONTENTS

  21. PROC PRINT • In the Editor window, type: proc print data=a.class2; run; • Submit for processing

  22. PROC PRINT

  23. Manipulating Data • Usually done within a data step • Match data sets using a shared key variable • Create new variables, or drop/rename existing variables • Take one or more subsets of the data • Sort the data by specific variable(s). • Overwrite existing or create new datasets • PROC SORT • Adding/Removing variables • Merging Datasets

  24. PROC SORT • In the Editor window, type: proc sort data=a.class2; by age descending weight height; run; • Submit for processing • WARNING: PROC SORT alters data • Store in a new dataset out=‘newdatasetname’;

  25. Adding/Removing variables • Create new data set, compute new variables, remove unwanted variables data a.class2metric (drop=weight height sex age); set a.class2; height_cm=height*2.54; weight_kg=weight/2.2; label height_cm=‘Height in CM’ weight_kg=‘Weight in Kilograms’; run; • Submit for processing

  26. Merging Datasets • Data sets must be sorted by the same key variable(s) proc sort data=a.class2; by name; proc sort data=a.class2metric; by name; data a.classmerged; merge a.class2 a.class2metric; by name; run; • Submit for processing

  27. Merging Datasets

  28. Descriptive Statistics • PROC FREQ • Produces a table of counts and percentages • For cross-tabulations, statistical tests can also be performed; e.g., independence testing • PROC MEANS • Produces descriptive statistics such as mean, standard deviation, minimum, maximum

  29. PROC FREQ • In the Editor window, type proc freq data=a.class2; tables age*sex; run; • Submit for processing

  30. PROC FREQ

  31. PROC MEANS • In the Editor window, type proc means data=a.class2; var age weight height; run; • Submit for processing

  32. PROC MEANS

  33. Graphing DataPROC GPLOT • Simple bivariate scatterplot • Separate lines • Multiple variables scatterplot • Options

  34. PROC GPLOT • Simple bivariate scatterplot: proc gplot data=a.class2; symbol1 value=dot interpol=rl; plot weight*height; run; • Submit for processing

  35. PROC GPLOT

  36. PROC GPLOT • To graph separate lines for each level of a categorical variable, type: proc gplot data=a.class2; symbol1 value=dot interpol=rl; plot weight*height = sex; run; • Submit for processing

  37. PROC GPLOT

  38. PROC GPLOT • Multiple variables on the same graph: proc gplot data=a.class2; symbol1 value=dot interpol=rl color=blue; symbol2 value=dot interpol=rl color=red; plot weight * age; plot2 height * age; run; quit; • Submit for processing

  39. PROC GPLOT

  40. value=___ Any character enclosed in single quotes Special characters dot plus sign star square ...and many others interpol=___ RL / RQ / RC linear quadratic cubic regression curves JOIN connects consecutive points (line graph) BOX PROC GPLOT

  41. Statistics in SAS • PROC CORR • Correlational analyses • PROC REG • Statistical Regression • PROC UNIVARIATE • To assess normality of regression residuals

  42. PROC CORR • Compute bivariate correlation coefficients proc corr data = a.class2; var age; with height weight; run;

  43. PROC CORR

  44. PROC REG Run a regression on merged ‘class’ dataset Save residuals and predicted values in an output dataset Request residual plot proc reg data=a.classmerged; model height_cm=age weight / partial; output out=reg_data p=predict r=resid rstudent=rstudent; plot rstudent. * height_cm; run; quit; Notes – the quit command terminates the regression procedure; otherwise it keeps running; the output data set will be in the work library, since no library was specified.

  45. PROC REG

  46. PROC REG

  47. PROC UNIVARIATE • Assess normality of regression residuals stored in the output dataset from PROC REG: proc univariate data=reg_data; var rstudent; histogram; qqplot / normal (mu=est sigma=est); run; quit;

  48. PROC UNIVARIATE

  49. PROC UNIVARIATE

  50. QUESTIONS

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